Category: Robotics

This is the second post about robot motion planning. You can find the first post about sampling-based planners over here. Being able to plan a path quickly while avoiding collisions is crucial for our roadmap.

To use a robot, you need to be able to plan a path from point A to point B — bonus points for not hitting anything. This is the cornerstone of our roadmap for robotics. Here’s a quick overview of the different ways to achieve this.

As discussed in the OMPL primer, there are different families of planning algorithms. In this first post we’ll focus on sampling-based planning.

To deliver the best hands in the world, we’ve collaborated with the best researchers — but what does it take to reach out of a research oriented market? To solve real world problems using robots? We’re convinced that tailoring a custom solution for each problem is not the way forward. We want the people facing those problems to be able to use our solutions themselves. And we have a roadmap to get there.